Triple
T17822753
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Darth Plagueis |
E445028
|
entity |
| Predicate | firstFullAppearanceYear |
P121574
|
FINISHED |
| Object | 2012 |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: 2012 | Statement: [Darth Plagueis, firstFullAppearanceYear, 2012]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: firstFullAppearanceYear Context triple: [Darth Plagueis, firstFullAppearanceYear, 2012]
-
A.
firstPublicationYearOfAppearance
Indicates the year in which an entity (such as a work or character) first appeared in a published form.
-
B.
firstAppearedAt
Indicates the point in time or specific event at which an entity was first introduced, observed, or became known.
-
C.
firstAppeared
Indicates the earliest known time or context in which an entity was introduced, observed, or came into existence.
-
D.
firstAppearanceApprox
Indicates that one entity is the approximate or estimated first appearance of another entity in time or context.
-
E.
yearOfFirstApparition
chosen
Indicates the calendar year in which the entity first appeared or was introduced.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d8b9f0de78819099395b14db75a8a6 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e4891282a081908d384d45bf444baf |
completed | April 19, 2026, 7:49 a.m. |
| PD | Predicate disambiguation | batch_69e3d8e266888190ae976b4b7d5b886f |
completed | April 18, 2026, 7:17 p.m. |
Created at: April 10, 2026, 10:15 a.m.